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Fine-grained Emotional Control of Text-To-Speech: Learning To Rank Inter- And Intra-Class Emotion Intensities
Shijun Wang, Jón Guðnason, Damian Borth
State-of-the-art Text-To-Speech (TTS) models are capable of producing high-quality speech. The generated speech, however, is usually neutral in emotional expression, whereas very o…
Generative Data Augmentation Guided by Triplet Loss for Speech Emotion Recognition
Shijun Wang, Hamed Hemati, Jón Guðnason +1
Speech Emotion Recognition (SER) is crucial for human-computer interaction but still remains a challenging problem because of two major obstacles: data scarcity and imbalance. Many…
Zero-shot Voice Conversion via Self-supervised Prosody Representation Learning
Shijun Wang, Dimche Kostadinov, Damian Borth
Voice Conversion (VC) for unseen speakers, also known as zero-shot VC, is an attractive research topic as it enables a range of applications like voice customizing, animation produ…
NoiseVC: Towards High Quality Zero-Shot Voice Conversion
Shijun Wang, Damian Borth
Voice conversion (VC) is a task that transforms voice from target audio to source without losing linguistic contents, it is challenging especially when source and target speakers a…
Using IPA-Based Tacotron for Data Efficient Cross-Lingual Speaker Adaptation and Pronunciation Enhancement
Hamed Hemati, Damian Borth
Recent neural Text-to-Speech (TTS) models have been shown to perform very well when enough data is available. However, fine-tuning them for new speakers or languages is not straigh…
AudioPairBank: Towards A Large-Scale Tag-Pair-Based Audio Content Analysis
Sebastian Sager, Benjamin Elizalde, Damian Borth +3
Recently, sound recognition has been used to identify sounds, such as car and river. However, sounds have nuances that may be better described by adjective-noun pairs such as slow…